Blur identification from vector quantizer encoder distortion
| Autor: | Bobby R. Hunt, Michael W. Marcellin, D.G. Sheppard, K. Panchapakesan |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Vector quantization Codebook Gaussian blur Image processing Data_CODINGANDINFORMATIONTHEORY Function (mathematics) Computer Graphics and Computer-Aided Design Image (mathematics) Set (abstract data type) symbols.namesake Signal-to-noise ratio Distortion Discrete cosine transform symbols Computer vision Artificial intelligence business Encoder Software Image restoration Mathematics |
| Zdroj: | ICIP (3) |
| ISSN: | 1057-7149 |
| Popis: | Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates. Each of these sets is used to train a vector quantizer codebook. Given an image degraded by unknown blur, it is first encoded with each of these codebooks. The blur in the image is then estimated by choosing from among the candidates, the one corresponding to the codebook that provides the lowest encoder distortion. Simulations are performed at various bit rates and with different levels of noise. Results show that the method performs well even at a signal-to-noise ratio (SNR) as low as 10 dB. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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